Classification using NaiveBayse Algorithm using Python

#Import Library of Gaussian Naive Bayes model
from sklearn.naive_bayes import GaussianNB
import numpy as np

import pandas as pd

#assigning predictor and target variables
dataF = pd.DataFrame()

dataF["x1"] = np.random.random_sample((5,)) + 1

dataF["x2"] = np.random.random_sample((5,)) + 2 

dataL = pd.DataFrame()

dataL = np.array([3, 3, 4, 3, 4])

#Create a Gaussian Classifier
model = GaussianNB()

# Train the model using the training sets 
model.fit(dataF, dataL)

#Predict Output 
predicted= model.predict([[1,2],[3,4]])
print(predicted)

Output: ([3,4])

-Thank you 

Basic Panda's functions for dataset manipulation in Python

Let we have two tables
Data1.schema:
    name:
    Company:
    Address:
Data2.schema:
    Name:
    gender:
    Age:

Commands:

# delete a column

del Data1[“name”]

# Return all the rows of the ‘name’ column

data1.loc[:, ‘name’]

# Return all the rows from ‘name’ column to ‘Company’ column

data1.loc[:, ‘name’:’Company’]

# Return all the rows from ‘Company’ column to ‘Address’ column

data1.loc[:, ‘Company’:’Address’]

# Return the rows whose Address is “India”

data1.loc[lambda var: var.Address == “India”, : ]

or

data1.loc[data1[“Address”] == “India”, :]

# Return only the name whose Address is “India”

data1.loc[lambda var: var.Address == “India”, ’name’]

# Return only the first five names whose Address is “India”

data1.loc[lambda var: var.Address == “India”, ’name’].head(5)

# Return only the last five names whose Address is “India”

data1.loc[lambda var: var.Address == “India”, ’name’].tail(5)

# Return the list of unique Addresses

data1.Address.unique()

# Return the list of unique Companies

data1.Company.unique()

# Return the list of unique names

data1.name.unique()

# Return the first unique Address

data1.Address.unique()[0]

# Return the total number of unique Addresses

data1.Address.unique().size

# Return the name, age and gender from data2 if the Address is India

t = data1.loc[data1[“Address”] == “India”, “name”].unique()
for x in range(0,t.size)
    data[2].loc[data2[“name”]==t[x],:]